Bioautomation

Volume 28, Issue 4 The Issue 4 is dedicated to the 85-th Anniversary of Prof. Stoyan Tzonkov

Editorial
85-th Anniversary of Prof. Stoyan Tzonkov, D.Sc., Honorary Chief Editor of the International Journal Bioautomation
Multistep Modelling and Monitoring of Bioprocesses	185-196
Velislava Lyubenova, Maya Ignatova, Denitsa Kristeva, Olympia Roeva
doi: 10.7546/ijba.2024.28.4.001033
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Velislava Lyubenova, Maya Ignatova, Denitsa Kristeva, Olympia Roeva (2024) Multistep Modelling and Monitoring of Bioprocesses, Int J Bioautomation, 28 (4), 185-196, doi: 10.7546/ijba.2024.28.4.001033
Abstract: A new approach is proposed for modelling and monitoring bioprocesses dynamics characterized by different metabolic states. Bioprocesses cannot be described by a single model. For this reason, three phases characterised by the bioprocess are defined – periodic, exponential, and stationary. During each phase, the process passes through one or more physiological states. Each physiological state is described by a sub-model with a different structure and parameter values. The transition of the process from one physiological state to another is carried out by switching the sub-models based on a predefined key parameter. Monitoring is performed by a cascade of software sensors using the sub-models and real-time measurement of the concentrations of the main process variables. The proposed approach was tested by modelling and monitoring the Escherichia coli phytase production process. Keywords: Modelling, Monitoring, Bioprocesses, Escherichia coli
Static Characteristics of the Anaerobic Digestion of Organic Wastes with Production of Hydrogen and Methane, Including Substrate Inhibition Influence	197-204
Elena Chorukova, Lyudmila Kabaivanova
doi: 10.7546/ijba.2024.28.4.001035
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Elena Chorukova, Lyudmila Kabaivanova (2024) Static Characteristics of the Anaerobic Digestion of Organic Wastes with Production of Hydrogen and Methane, Including Substrate Inhibition Influence, Int J Bioautomation, 28 (4), 197-204, doi: 10.7546/ijba.2024.28.4.001035
Abstract: In this paper, static characteristics of a simple mathematical model of a two-stage anaerobic digestion (AD) process for sequential production of hydrogen (Н₂) and methane (СН₄) are derived. The influence of substrate inhibition in the first and second bioreactors is considered. Different process variables are described in the considered mathematical model. The concentration of the influent organic matter is assumed to be the main external disturbance reflected in the model. The obtained input-output static characteristics for the energy carriers (H₂ and CH₄) could be used to control and optimize the subjected process. Keywords: Two-stage anaerobic digestion, Mathematical model, Static characteristics, Substrate inhibition
Neuro-dynamic Programming to Optimal Control of a Biotechnological Process	205-220
Tatiana Ilkova, Mitko Petrov
doi: 10.7546/ijba.2024.28.4.001036
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Tatiana Ilkova, Mitko Petrov (2024) Neuro-dynamic Programming to Optimal Control of a Biotechnological Process, Int J Bioautomation, 28 (4), 205-220, doi: 10.7546/ijba.2024.28.4.001036
Abstract: Dynamic programming (DP) is an elegant way to solve problems related to optimization and optimal control of processes. DP, however, has one major drawback, namely the “curse of dimensionality”. To overcome this shortcoming, an approach called neuro-dynamic programming (NDP) has been developed. This approach solves the “curse of dimensionality” problem of DP. For this purpose, a neural network is used in NDP, which ignores the poor results of the utility criterion. In this way, the time for solving the specific task is significantly shortened. In this work, an NDP algorithm is presented for the optimal control of a fed-batch biotechnological process for the production of L-lysine by the strain Brevibacterium flavum 22LD. Application of the NDP algorithm ensures maximum productivity of the L-lysine. Keywords: Optimal control, Dynamic programming, Neuro-dynamic programming, Fed-batch biotechnological processes, L-lysine production, Brevibacterium flavum 22LD strain
System Analysis Theory Applied for Development of Microalgae Processes and Photobioreactors in the Frame of Integral Biorefinery Concept	221-232
Alexander Dimitrov Kroumov, Maya Margaritova Zaharieva, Hristo Miladinov Najdenski
doi: 10.7546/ijba.2024.28.4.001037
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Alexander Dimitrov Kroumov, Maya Margaritova Zaharieva, Hristo Miladinov Najdenski (2024) System Analysis Theory Applied for Development of Microalgae Processes and Photobioreactors in the Frame of Integral Biorefinery Concept, Int J Bioautomation, 28 (4), 221-232, doi: 10.7546/ijba.2024.28.4.001037
Abstract: Microalgae technology involves many steps of unit operations and is connected with global warming and pandemic problems because of the unique features of algal cells. Studying sophisticated systems cannot be without special mathematical tools and approaches that combine knowledge from many research areas. The system analysis theory applied in biotechnology with great success can be applied by principles of analogy to microalgae cultivation of cells in CO₂ fixation from flue gases in innovative closed photobioreactors (PBRs) where the products of biomass can have performed antimicrobial, anticancer, antiviral and other activities by challenging chemical agents. Recently, a multifunctional algology laboratory was created at the Stephan Angeloff Institute of Microbiology by applying knowledge in state of the art. The goal of this work was to summarize the 40 years of experience of the authors in this area and to show how this was realized by innovative engineering solutions for studying and developing the microalgae system. Special attention was paid to the development of hybrid, innovative PBRs with the aim of fully revealing the potential of microalgae strains not only for the complete absorption of CO₂ from flue gases but also for the synthesis of high-value products (HVP) with antimicrobial, antiviral and anticancer activity. Keywords: Photobioreactors, System analysis, Biorefinery concept, Microalgae
Influence of Genetic Algorithm Parameters on Their Performance for Parameter Identification of a Yeast Fed-batch Fermentation Process Model	233-244
Maria Angelova, Tania Pencheva
doi: 10.7546/ijba.2024.28.4.001038
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Maria Angelova, Tania Pencheva (2024) Influence of Genetic Algorithm Parameters on Their Performance for Parameter Identification of a Yeast Fed-batch Fermentation Process Model, Int J Bioautomation, 28 (4), 233-244, doi: 10.7546/ijba.2024.28.4.001038
Abstract: Eight single (SGA) and eight multi-population (MGA) genetic algorithms (GA) differing in the sequence of implementation of the main genetic operators’ selection, crossover and mutation, or omitting the mutation operator, have been examined for the purposes of parameter identification of a Saccharomyces cerevisiae fed-batch fermentation process model. The influence of some of the main genetic algorithm parameters, namely number of individuals, maximum number of generations, generation gap, crossover and mutation rates for both SGA and MGA, and insertion and migration probability for MGA only, have been investigated in depth. Almost all applied SGA and MGA led to similar values of the optimization criterion but the SGA with operators’ sequence mutation, crossover and selection, and MGA with operators’ sequence crossover, selection and mutation, are significantly faster than others while keeping the model accuracy. Among the considered GA parameters, generation gap influences most significantly to SGA and MGA convergence time, saving of about 40% of computational time of the algorithms without affecting the model accuracy. Keywords: Single genetic algorithms, Multi-population genetic algorithms, Parameter identification, Fed-batch fermentation process model, Saccharomyces cerevisiae
A Comparison of Chaotic Electromagnetic Field Optimization Algorithms	245-265
Olympia Roeva, Dafina Zoteva
doi: 10.7546/ijba.2024.28.4.000970
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Olympia Roeva, Dafina Zoteva (2024) A Comparison of Chaotic Electromagnetic Field Optimization Algorithms, Int J Bioautomation, 28 (4), 245-265, doi: 10.7546/ijba.2024.28.4.000970
Abstract: This paper investigates the performance of various Electromagnetic Field Optimization (EFO) algorithms. Chaos maps are proposed to improve the performance of EFO algorithms. Ten chaotic maps are incorporated in EFO – Chebyshev, Circle, Gauss, Iterative, Logistic, Piecewise, Sine, Singer, Sinusoidal and Tent. To compare the performance of the constructed EFO algorithms, a case study of the identification of the model parameters of a cultivation process model is studied. An experimental data set from E. coli BL21(DE3)pPhyt109 fed-batch cultivation process is used. Based on the results of 30 runs of each EFO, some statistical and InterCriteria analyzes are performed. As a result, the best performing EFO algorithms are iterative EFO and tent chaotic map EFO. These algorithms gave the best objective value (best and mean value) and had a good distribution of results. Keywords: Chaotic maps, Electromagnetic field optimization, InterCriteria analysis, E. coli BL21(DE3)pPhyt109 fed-batch cultivation
“Pure” or “Numerical” Jordan Form?	267-271
Ivan Popchev
doi: 10.7546/ijba.2024.28.4.001039
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Ivan Popchev (2024) “Pure” or “Numerical” Jordan Form?, Int J Bioautomation, 28 (4), 267-271, doi: 10.7546/ijba.2024.28.4.001039
Abstract: Keywords: Book Review